Overview

Dataset statistics

Number of variables17
Number of observations42931
Missing cells63506
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory136.0 B

Variable types

Numeric10
Text4
Categorical2
DateTime1

Alerts

host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
minimum_nights is highly overall correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
reviews_per_month is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
last_review has 10304 (24.0%) missing valuesMissing
reviews_per_month has 10304 (24.0%) missing valuesMissing
detailed_location has 42881 (99.9%) missing valuesMissing
price is highly skewed (γ1 = 81.45449106)Skewed
id has unique valuesUnique
number_of_reviews has 10304 (24.0%) zerosZeros
number_of_reviews_ltm has 21470 (50.0%) zerosZeros

Reproduction

Analysis started2024-05-07 06:13:57.996604
Analysis finished2024-05-07 06:14:32.812553
Duration34.82 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42931
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2227724 × 1017
Minimum2595
Maximum8.4046605 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:33.111735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2595
5-th percentile3039221
Q119404736
median43374815
Q36.305016 × 1017
95-th percentile8.076607 × 1017
Maximum8.4046605 × 1017
Range8.4046605 × 1017
Interquartile range (IQR)6.305016 × 1017

Descriptive statistics

Standard deviation3.344213 × 1017
Coefficient of variation (CV)1.5045233
Kurtosis-1.1494006
Mean2.2227724 × 1017
Median Absolute Deviation (MAD)29842097
Skewness0.87882551
Sum5.6176987 × 1018
Variance1.1183761 × 1035
MonotonicityNot monotonic
2024-05-07T06:14:33.420237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2595 1
 
< 0.1%
53574639 1
 
< 0.1%
53605871 1
 
< 0.1%
53605960 1
 
< 0.1%
53606101 1
 
< 0.1%
53606289 1
 
< 0.1%
53575004 1
 
< 0.1%
53606306 1
 
< 0.1%
53606376 1
 
< 0.1%
53606452 1
 
< 0.1%
Other values (42921) 42921
> 99.9%
ValueCountFrequency (%)
2595 1
< 0.1%
5121 1
< 0.1%
5136 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5586 1
< 0.1%
5803 1
< 0.1%
6848 1
< 0.1%
6872 1
< 0.1%
6990 1
< 0.1%
ValueCountFrequency (%)
8.404660471 × 10171
< 0.1%
8.402288788 × 10171
< 0.1%
8.401503224 × 10171
< 0.1%
8.400804342 × 10171
< 0.1%
8.40049771 × 10171
< 0.1%
8.400478127 × 10171
< 0.1%
8.400315504 × 10171
< 0.1%
8.399770476 × 10171
< 0.1%
8.39959564 × 10171
< 0.1%
8.399557651 × 10171
< 0.1%

name
Text

Distinct41408
Distinct (%)96.5%
Missing12
Missing (%)< 0.1%
Memory size335.5 KiB
2024-05-07T06:14:34.149423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length248
Median length114
Mean length37.643794
Min length1

Characters and Unicode

Total characters1615634
Distinct characters838
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40591 ?
Unique (%)94.6%

Sample

1st rowSkylit Midtown Castle
2nd rowBlissArtsSpace!
3rd rowCozy Clean Guest Room - Family Apt
4th rowLarge Furnished Room Near B'way 
5th rowLarge Sunny Brooklyn Duplex, Patio + Garden
ValueCountFrequency (%)
in 13926
 
5.2%
room 7890
 
2.9%
7235
 
2.7%
bedroom 7142
 
2.7%
private 6183
 
2.3%
apartment 5696
 
2.1%
cozy 4040
 
1.5%
studio 3712
 
1.4%
apt 3629
 
1.3%
the 3612
 
1.3%
Other values (12721) 206018
76.6%
2024-05-07T06:14:35.219056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
227633
 
14.1%
e 118241
 
7.3%
o 112670
 
7.0%
t 94796
 
5.9%
a 91102
 
5.6%
r 88976
 
5.5%
n 84848
 
5.3%
i 84620
 
5.2%
l 46838
 
2.9%
m 43858
 
2.7%
Other values (828) 622052
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1615634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
227633
 
14.1%
e 118241
 
7.3%
o 112670
 
7.0%
t 94796
 
5.9%
a 91102
 
5.6%
r 88976
 
5.5%
n 84848
 
5.3%
i 84620
 
5.2%
l 46838
 
2.9%
m 43858
 
2.7%
Other values (828) 622052
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1615634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
227633
 
14.1%
e 118241
 
7.3%
o 112670
 
7.0%
t 94796
 
5.9%
a 91102
 
5.6%
r 88976
 
5.5%
n 84848
 
5.3%
i 84620
 
5.2%
l 46838
 
2.9%
m 43858
 
2.7%
Other values (828) 622052
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1615634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
227633
 
14.1%
e 118241
 
7.3%
o 112670
 
7.0%
t 94796
 
5.9%
a 91102
 
5.6%
r 88976
 
5.5%
n 84848
 
5.3%
i 84620
 
5.2%
l 46838
 
2.9%
m 43858
 
2.7%
Other values (828) 622052
38.5%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct27455
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5160121 × 108
Minimum1678
Maximum5.0387289 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:35.542328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1678
5-th percentile1398639
Q116085328
median74338125
Q32.6806924 × 108
95-th percentile4.7498775 × 108
Maximum5.0387289 × 108
Range5.0387121 × 108
Interquartile range (IQR)2.5198391 × 108

Descriptive statistics

Standard deviation1.6213011 × 108
Coefficient of variation (CV)1.0694513
Kurtosis-0.70164463
Mean1.5160121 × 108
Median Absolute Deviation (MAD)70621479
Skewness0.86158061
Sum6.5083915 × 1012
Variance2.6286172 × 1016
MonotonicityNot monotonic
2024-05-07T06:14:35.843896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107434423 526
 
1.2%
3223938 394
 
0.9%
496944100 356
 
0.8%
305240193 222
 
0.5%
19303369 207
 
0.5%
200239515 192
 
0.4%
204704622 178
 
0.4%
162280872 159
 
0.4%
51501835 131
 
0.3%
137358866 124
 
0.3%
Other values (27445) 40442
94.2%
ValueCountFrequency (%)
1678 1
 
< 0.1%
2234 1
 
< 0.1%
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2782 1
 
< 0.1%
2787 7
< 0.1%
2845 3
< 0.1%
2868 1
 
< 0.1%
3647 1
 
< 0.1%
3757 1
 
< 0.1%
ValueCountFrequency (%)
503872891 1
< 0.1%
503757175 1
< 0.1%
503726515 1
< 0.1%
503718879 1
< 0.1%
503593397 1
< 0.1%
503579221 1
< 0.1%
503563559 1
< 0.1%
503556906 1
< 0.1%
503542653 1
< 0.1%
503540971 1
< 0.1%
Distinct9831
Distinct (%)22.9%
Missing5
Missing (%)< 0.1%
Memory size335.5 KiB
2024-05-07T06:14:36.253470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length33
Mean length6.5092951
Min length1

Characters and Unicode

Total characters279418
Distinct characters151
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5667 ?
Unique (%)13.2%

Sample

1st rowJennifer
2nd rowGaron
3rd rowMaryEllen
4th rowShunichi
5th rowRebecca
ValueCountFrequency (%)
614
 
1.3%
roompicks 608
 
1.2%
blueground 526
 
1.1%
and 497
 
1.0%
eugene 400
 
0.8%
michael 346
 
0.7%
david 326
 
0.7%
alex 256
 
0.5%
kristina 256
 
0.5%
by 233
 
0.5%
Other values (9170) 44983
91.7%
2024-05-07T06:14:37.002836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 32451
 
11.6%
e 26336
 
9.4%
i 22166
 
7.9%
n 22036
 
7.9%
r 16248
 
5.8%
o 14270
 
5.1%
l 13690
 
4.9%
s 9277
 
3.3%
t 9041
 
3.2%
h 8220
 
2.9%
Other values (141) 105683
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 32451
 
11.6%
e 26336
 
9.4%
i 22166
 
7.9%
n 22036
 
7.9%
r 16248
 
5.8%
o 14270
 
5.1%
l 13690
 
4.9%
s 9277
 
3.3%
t 9041
 
3.2%
h 8220
 
2.9%
Other values (141) 105683
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 32451
 
11.6%
e 26336
 
9.4%
i 22166
 
7.9%
n 22036
 
7.9%
r 16248
 
5.8%
o 14270
 
5.1%
l 13690
 
4.9%
s 9277
 
3.3%
t 9041
 
3.2%
h 8220
 
2.9%
Other values (141) 105683
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 32451
 
11.6%
e 26336
 
9.4%
i 22166
 
7.9%
n 22036
 
7.9%
r 16248
 
5.8%
o 14270
 
5.1%
l 13690
 
4.9%
s 9277
 
3.3%
t 9041
 
3.2%
h 8220
 
2.9%
Other values (141) 105683
37.8%

neighbourhood_group
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.5 KiB
Manhattan
17658 
Brooklyn
16237 
Queens
6916 
Bronx
 
1691
Staten Island
 
429

Length

Max length13
Median length9
Mean length8.0209173
Min length5

Characters and Unicode

Total characters344346
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManhattan
2nd rowBrooklyn
3rd rowManhattan
4th rowManhattan
5th rowBrooklyn

Common Values

ValueCountFrequency (%)
Manhattan 17658
41.1%
Brooklyn 16237
37.8%
Queens 6916
 
16.1%
Bronx 1691
 
3.9%
Staten Island 429
 
1.0%

Length

2024-05-07T06:14:37.334705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T06:14:37.655298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 17658
40.7%
brooklyn 16237
37.4%
queens 6916
 
16.0%
bronx 1691
 
3.9%
staten 429
 
1.0%
island 429
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n 61018
17.7%
a 53832
15.6%
t 36174
10.5%
o 34165
9.9%
B 17928
 
5.2%
r 17928
 
5.2%
M 17658
 
5.1%
h 17658
 
5.1%
l 16666
 
4.8%
y 16237
 
4.7%
Other values (10) 55082
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 344346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 61018
17.7%
a 53832
15.6%
t 36174
10.5%
o 34165
9.9%
B 17928
 
5.2%
r 17928
 
5.2%
M 17658
 
5.1%
h 17658
 
5.1%
l 16666
 
4.8%
y 16237
 
4.7%
Other values (10) 55082
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 344346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 61018
17.7%
a 53832
15.6%
t 36174
10.5%
o 34165
9.9%
B 17928
 
5.2%
r 17928
 
5.2%
M 17658
 
5.1%
h 17658
 
5.1%
l 16666
 
4.8%
y 16237
 
4.7%
Other values (10) 55082
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 344346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 61018
17.7%
a 53832
15.6%
t 36174
10.5%
o 34165
9.9%
B 17928
 
5.2%
r 17928
 
5.2%
M 17658
 
5.1%
h 17658
 
5.1%
l 16666
 
4.8%
y 16237
 
4.7%
Other values (10) 55082
16.0%
Distinct223
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:38.199034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length17
Mean length11.700147
Min length4

Characters and Unicode

Total characters502299
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowMidtown
2nd rowBedford-Stuyvesant
3rd rowUpper West Side
4th rowMidtown
5th rowSunset Park
ValueCountFrequency (%)
east 5412
 
7.9%
side 3673
 
5.3%
bedford-stuyvesant 3086
 
4.5%
upper 2973
 
4.3%
harlem 2935
 
4.3%
heights 2914
 
4.2%
williamsburg 2597
 
3.8%
midtown 2182
 
3.2%
village 2116
 
3.1%
west 2110
 
3.1%
Other values (236) 38922
56.5%
2024-05-07T06:14:39.676181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 45003
 
9.0%
i 35535
 
7.1%
s 34196
 
6.8%
t 34054
 
6.8%
a 33155
 
6.6%
r 28945
 
5.8%
l 28068
 
5.6%
25989
 
5.2%
n 23566
 
4.7%
o 22306
 
4.4%
Other values (44) 191482
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 502299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 45003
 
9.0%
i 35535
 
7.1%
s 34196
 
6.8%
t 34054
 
6.8%
a 33155
 
6.6%
r 28945
 
5.8%
l 28068
 
5.6%
25989
 
5.2%
n 23566
 
4.7%
o 22306
 
4.4%
Other values (44) 191482
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 502299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 45003
 
9.0%
i 35535
 
7.1%
s 34196
 
6.8%
t 34054
 
6.8%
a 33155
 
6.6%
r 28945
 
5.8%
l 28068
 
5.6%
25989
 
5.2%
n 23566
 
4.7%
o 22306
 
4.4%
Other values (44) 191482
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 502299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 45003
 
9.0%
i 35535
 
7.1%
s 34196
 
6.8%
t 34054
 
6.8%
a 33155
 
6.6%
r 28945
 
5.8%
l 28068
 
5.6%
25989
 
5.2%
n 23566
 
4.7%
o 22306
 
4.4%
Other values (44) 191482
38.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct22673
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728273
Minimum40.500314
Maximum40.91138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:40.131145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40.500314
5-th percentile40.640268
Q140.687485
median40.72404
Q340.762293
95-th percentile40.82956
Maximum40.91138
Range0.41106557
Interquartile range (IQR)0.074808455

Descriptive statistics

Standard deviation0.057640457
Coefficient of variation (CV)0.0014152443
Kurtosis0.13647495
Mean40.728273
Median Absolute Deviation (MAD)0.03747
Skewness0.2179238
Sum1748505.5
Variance0.0033224223
MonotonicityNot monotonic
2024-05-07T06:14:40.649529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.76153 52
 
0.1%
40.76411 32
 
0.1%
40.760452 24
 
0.1%
40.761532 22
 
0.1%
40.7607614 22
 
0.1%
40.73756 19
 
< 0.1%
40.75436 19
 
< 0.1%
40.71899 18
 
< 0.1%
40.74926 17
 
< 0.1%
40.71579 17
 
< 0.1%
Other values (22663) 42689
99.4%
ValueCountFrequency (%)
40.50031443 1
< 0.1%
40.50456 1
< 0.1%
40.507114 1
< 0.1%
40.50848895 1
< 0.1%
40.50863 1
< 0.1%
40.52034 1
< 0.1%
40.52224 1
< 0.1%
40.52339 1
< 0.1%
40.52498 1
< 0.1%
40.53125 1
< 0.1%
ValueCountFrequency (%)
40.91138 1
< 0.1%
40.91114684 1
< 0.1%
40.91062 1
< 0.1%
40.90884 1
< 0.1%
40.90530196 1
< 0.1%
40.90505 1
< 0.1%
40.90424 1
< 0.1%
40.90421 1
< 0.1%
40.90403 1
< 0.1%
40.90376 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct20130
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.943665
Minimum-74.251907
Maximum-73.71087
Zeros0
Zeros (%)0.0%
Negative42931
Negative (%)100.0%
Memory size335.5 KiB
2024-05-07T06:14:40.934041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-74.251907
5-th percentile-74.003735
Q1-73.98175
median-73.95262
Q3-73.924035
95-th percentile-73.820533
Maximum-73.71087
Range0.541037
Interquartile range (IQR)0.057715

Descriptive statistics

Standard deviation0.056626547
Coefficient of variation (CV)-0.0007658066
Kurtosis3.0410896
Mean-73.943665
Median Absolute Deviation (MAD)0.02895
Skewness1.1475906
Sum-3174475.5
Variance0.0032065658
MonotonicityNot monotonic
2024-05-07T06:14:41.241820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.99878 52
 
0.1%
-73.99371 30
 
0.1%
-73.99391 24
 
0.1%
-73.76606 24
 
0.1%
-73.97643 24
 
0.1%
-73.9861055 22
 
0.1%
-74.00587 22
 
0.1%
-73.998779 22
 
0.1%
-73.9535 20
 
< 0.1%
-73.98698 18
 
< 0.1%
Other values (20120) 42673
99.4%
ValueCountFrequency (%)
-74.251907 1
< 0.1%
-74.24984 1
< 0.1%
-74.24308594 1
< 0.1%
-74.24135 1
< 0.1%
-74.23913565 1
< 0.1%
-74.21514 1
< 0.1%
-74.21126 1
< 0.1%
-74.21088 1
< 0.1%
-74.21065716 1
< 0.1%
-74.20739 1
< 0.1%
ValueCountFrequency (%)
-73.71087 1
< 0.1%
-73.71299 1
< 0.1%
-73.71439 1
< 0.1%
-73.7174099 1
< 0.1%
-73.71974787 1
< 0.1%
-73.71991 1
< 0.1%
-73.72054 1
< 0.1%
-73.7216024 1
< 0.1%
-73.72408 1
< 0.1%
-73.72434 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.5 KiB
Entire home/apt
24279 
Private room
17879 
Shared room
 
576
Hotel room
 
197

Length

Max length15
Median length15
Mean length13.674012
Min length10

Characters and Unicode

Total characters587039
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowPrivate room
3rd rowPrivate room
4th rowPrivate room
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 24279
56.6%
Private room 17879
41.6%
Shared room 576
 
1.3%
Hotel room 197
 
0.5%

Length

2024-05-07T06:14:41.562839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T06:14:41.832189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 24279
28.3%
home/apt 24279
28.3%
room 18652
21.7%
private 17879
20.8%
shared 576
 
0.7%
hotel 197
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 67210
11.4%
t 66634
11.4%
o 61780
10.5%
r 61386
10.5%
m 42931
 
7.3%
42931
 
7.3%
a 42734
 
7.3%
i 42158
 
7.2%
h 24855
 
4.2%
p 24279
 
4.1%
Other values (9) 110141
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 587039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 67210
11.4%
t 66634
11.4%
o 61780
10.5%
r 61386
10.5%
m 42931
 
7.3%
42931
 
7.3%
a 42734
 
7.3%
i 42158
 
7.2%
h 24855
 
4.2%
p 24279
 
4.1%
Other values (9) 110141
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 587039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 67210
11.4%
t 66634
11.4%
o 61780
10.5%
r 61386
10.5%
m 42931
 
7.3%
42931
 
7.3%
a 42734
 
7.3%
i 42158
 
7.2%
h 24855
 
4.2%
p 24279
 
4.1%
Other values (9) 110141
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 587039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 67210
11.4%
t 66634
11.4%
o 61780
10.5%
r 61386
10.5%
m 42931
 
7.3%
42931
 
7.3%
a 42734
 
7.3%
i 42158
 
7.2%
h 24855
 
4.2%
p 24279
 
4.1%
Other values (9) 110141
18.8%

price
Real number (ℝ)

SKEWED 

Distinct1089
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.30717
Minimum0
Maximum99000
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:42.094568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q175
median125
Q3200
95-th percentile499
Maximum99000
Range99000
Interquartile range (IQR)125

Descriptive statistics

Standard deviation895.08291
Coefficient of variation (CV)4.4685516
Kurtosis7897.8051
Mean200.30717
Median Absolute Deviation (MAD)59
Skewness81.454491
Sum8599387
Variance801173.42
MonotonicityNot monotonic
2024-05-07T06:14:42.391400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1254
 
2.9%
100 1144
 
2.7%
200 886
 
2.1%
120 784
 
1.8%
60 765
 
1.8%
75 746
 
1.7%
50 736
 
1.7%
80 728
 
1.7%
70 697
 
1.6%
90 691
 
1.6%
Other values (1079) 34500
80.4%
ValueCountFrequency (%)
0 27
0.1%
10 7
 
< 0.1%
15 2
 
< 0.1%
16 3
 
< 0.1%
18 2
 
< 0.1%
19 5
 
< 0.1%
20 18
< 0.1%
21 3
 
< 0.1%
22 15
< 0.1%
23 18
< 0.1%
ValueCountFrequency (%)
99000 1
 
< 0.1%
85170 1
 
< 0.1%
85100 1
 
< 0.1%
65115 1
 
< 0.1%
20500 1
 
< 0.1%
19750 1
 
< 0.1%
15000 1
 
< 0.1%
10000 9
< 0.1%
9999 4
< 0.1%
9994 1
 
< 0.1%

minimum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.111178
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:42.668613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q330
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)28

Descriptive statistics

Standard deviation27.462513
Coefficient of variation (CV)1.5163294
Kurtosis334.65305
Mean18.111178
Median Absolute Deviation (MAD)6
Skewness12.426192
Sum777531
Variance754.18962
MonotonicityNot monotonic
2024-05-07T06:14:42.958207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 18235
42.5%
1 7946
18.5%
2 5524
 
12.9%
3 3814
 
8.9%
5 1499
 
3.5%
4 1422
 
3.3%
7 922
 
2.1%
31 786
 
1.8%
90 472
 
1.1%
6 370
 
0.9%
Other values (118) 1941
 
4.5%
ValueCountFrequency (%)
1 7946
18.5%
2 5524
12.9%
3 3814
8.9%
4 1422
 
3.3%
5 1499
 
3.5%
6 370
 
0.9%
7 922
 
2.1%
8 53
 
0.1%
9 27
 
0.1%
10 218
 
0.5%
ValueCountFrequency (%)
1250 1
 
< 0.1%
1124 1
 
< 0.1%
1000 2
 
< 0.1%
999 1
 
< 0.1%
500 6
 
< 0.1%
480 1
 
< 0.1%
400 3
 
< 0.1%
370 1
 
< 0.1%
366 1
 
< 0.1%
365 51
0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct476
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.856001
Minimum0
Maximum1842
Zeros10304
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:43.242582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile129
Maximum1842
Range1842
Interquartile range (IQR)23

Descriptive statistics

Standard deviation56.616344
Coefficient of variation (CV)2.189679
Kurtosis73.000382
Mean25.856001
Median Absolute Deviation (MAD)5
Skewness5.7317373
Sum1110024
Variance3205.4104
MonotonicityNot monotonic
2024-05-07T06:14:43.547751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10304
24.0%
1 4410
 
10.3%
2 2894
 
6.7%
3 2052
 
4.8%
4 1577
 
3.7%
5 1289
 
3.0%
6 1110
 
2.6%
7 1002
 
2.3%
8 813
 
1.9%
9 751
 
1.7%
Other values (466) 16729
39.0%
ValueCountFrequency (%)
0 10304
24.0%
1 4410
10.3%
2 2894
 
6.7%
3 2052
 
4.8%
4 1577
 
3.7%
5 1289
 
3.0%
6 1110
 
2.6%
7 1002
 
2.3%
8 813
 
1.9%
9 751
 
1.7%
ValueCountFrequency (%)
1842 1
< 0.1%
1642 1
< 0.1%
1097 1
< 0.1%
1010 1
< 0.1%
985 1
< 0.1%
948 1
< 0.1%
917 1
< 0.1%
798 1
< 0.1%
789 1
< 0.1%
766 1
< 0.1%

last_review
Date

MISSING 

Distinct2795
Distinct (%)8.6%
Missing10304
Missing (%)24.0%
Memory size335.5 KiB
Minimum2011-05-12 00:00:00
Maximum2023-03-06 00:00:00
2024-05-07T06:14:43.859922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:44.150348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct881
Distinct (%)2.7%
Missing10304
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean1.1689883
Minimum0.01
Maximum86.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:44.469158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.14
median0.52
Q31.67
95-th percentile4.09
Maximum86.61
Range86.6
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation1.7896749
Coefficient of variation (CV)1.5309605
Kurtosis302.80799
Mean1.1689883
Median Absolute Deviation (MAD)0.47
Skewness10.30231
Sum38140.58
Variance3.2029362
MonotonicityNot monotonic
2024-05-07T06:14:44.770577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 1063
 
2.5%
0.01 965
 
2.2%
0.03 826
 
1.9%
0.05 779
 
1.8%
0.04 722
 
1.7%
0.07 595
 
1.4%
0.06 514
 
1.2%
0.1 493
 
1.1%
1 476
 
1.1%
0.08 466
 
1.1%
Other values (871) 25728
59.9%
(Missing) 10304
24.0%
ValueCountFrequency (%)
0.01 965
2.2%
0.02 1063
2.5%
0.03 826
1.9%
0.04 722
1.7%
0.05 779
1.8%
0.06 514
1.2%
0.07 595
1.4%
0.08 466
1.1%
0.09 459
1.1%
0.1 493
1.1%
ValueCountFrequency (%)
86.61 1
< 0.1%
61.26 1
< 0.1%
55.46 1
< 0.1%
51.71 1
< 0.1%
45.53 1
< 0.1%
43.75 1
< 0.1%
40.78 1
< 0.1%
36.94 1
< 0.1%
35.96 1
< 0.1%
33.81 1
< 0.1%
Distinct65
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.054809
Minimum1
Maximum526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:45.040369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile159
Maximum526
Range525
Interquartile range (IQR)3

Descriptive statistics

Standard deviation80.867958
Coefficient of variation (CV)3.3618209
Kurtosis22.085603
Mean24.054809
Median Absolute Deviation (MAD)0
Skewness4.6167703
Sum1032697
Variance6539.6266
MonotonicityNot monotonic
2024-05-07T06:14:45.337063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22528
52.5%
2 5666
 
13.2%
3 2814
 
6.6%
4 1740
 
4.1%
5 1020
 
2.4%
6 768
 
1.8%
8 560
 
1.3%
7 546
 
1.3%
526 526
 
1.2%
394 394
 
0.9%
Other values (55) 6369
 
14.8%
ValueCountFrequency (%)
1 22528
52.5%
2 5666
 
13.2%
3 2814
 
6.6%
4 1740
 
4.1%
5 1020
 
2.4%
6 768
 
1.8%
7 546
 
1.3%
8 560
 
1.3%
9 315
 
0.7%
10 320
 
0.7%
ValueCountFrequency (%)
526 526
1.2%
394 394
0.9%
356 356
0.8%
222 222
0.5%
207 207
 
0.5%
192 192
 
0.4%
178 178
 
0.4%
159 159
 
0.4%
131 131
 
0.3%
124 248
0.6%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7365074
Minimum0
Maximum1093
Zeros21470
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size335.5 KiB
2024-05-07T06:14:45.652390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile40
Maximum1093
Range1093
Interquartile range (IQR)7

Descriptive statistics

Standard deviation18.290256
Coefficient of variation (CV)2.3641489
Kurtosis584.39757
Mean7.7365074
Median Absolute Deviation (MAD)0
Skewness14.547605
Sum332136
Variance334.53346
MonotonicityNot monotonic
2024-05-07T06:14:45.943381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21470
50.0%
1 3377
 
7.9%
2 2238
 
5.2%
3 1711
 
4.0%
4 1226
 
2.9%
5 956
 
2.2%
6 755
 
1.8%
7 628
 
1.5%
8 532
 
1.2%
10 479
 
1.1%
Other values (158) 9559
22.3%
ValueCountFrequency (%)
0 21470
50.0%
1 3377
 
7.9%
2 2238
 
5.2%
3 1711
 
4.0%
4 1226
 
2.9%
5 956
 
2.2%
6 755
 
1.8%
7 628
 
1.5%
8 532
 
1.2%
9 457
 
1.1%
ValueCountFrequency (%)
1093 1
< 0.1%
889 1
< 0.1%
770 1
< 0.1%
668 1
< 0.1%
530 1
< 0.1%
458 1
< 0.1%
442 1
< 0.1%
379 1
< 0.1%
368 1
< 0.1%
354 1
< 0.1%

detailed_location
Text

MISSING 

Distinct50
Distinct (%)100.0%
Missing42881
Missing (%)99.9%
Memory size335.5 KiB
2024-05-07T06:14:46.487104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length87
Median length76
Mean length65.38
Min length56

Characters and Unicode

Total characters3269
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row104 West 40th Street, New York, NY 10018, United States of America
2nd row294 Gates Avenue, New York, NY 11216, United States of America
3rd row301 West 109th Street, New York, NY 10025, United States of America
4th rowHilton Garden Inn, 237 West 54th Street, New York, NY 10019, United States of America
5th rowAl-Noor School, 675 4th Avenue, New York, NY 11232, United States of America
ValueCountFrequency (%)
new 50
 
8.7%
york 50
 
8.7%
ny 50
 
8.7%
united 50
 
8.7%
states 50
 
8.7%
of 50
 
8.7%
america 50
 
8.7%
street 31
 
5.4%
avenue 16
 
2.8%
west 13
 
2.2%
Other values (142) 168
29.1%
2024-05-07T06:14:47.357993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
528
16.2%
e 332
 
10.2%
t 269
 
8.2%
, 155
 
4.7%
r 149
 
4.6%
1 139
 
4.3%
o 125
 
3.8%
a 121
 
3.7%
i 107
 
3.3%
N 102
 
3.1%
Other values (47) 1242
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
528
16.2%
e 332
 
10.2%
t 269
 
8.2%
, 155
 
4.7%
r 149
 
4.6%
1 139
 
4.3%
o 125
 
3.8%
a 121
 
3.7%
i 107
 
3.3%
N 102
 
3.1%
Other values (47) 1242
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
528
16.2%
e 332
 
10.2%
t 269
 
8.2%
, 155
 
4.7%
r 149
 
4.6%
1 139
 
4.3%
o 125
 
3.8%
a 121
 
3.7%
i 107
 
3.3%
N 102
 
3.1%
Other values (47) 1242
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
528
16.2%
e 332
 
10.2%
t 269
 
8.2%
, 155
 
4.7%
r 149
 
4.6%
1 139
 
4.3%
o 125
 
3.8%
a 121
 
3.7%
i 107
 
3.3%
N 102
 
3.1%
Other values (47) 1242
38.0%

Interactions

2024-05-07T06:14:28.693455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:03.168538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:05.820353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:08.362208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:11.023105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:14.534540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:17.205754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:19.996296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:22.607825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:25.182397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:28.927517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:03.420731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:06.055202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:08.609600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:11.295637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:14.913506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:17.664784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:20.254220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:22.852944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:25.483998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:29.189074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:03.801930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:06.333858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:08.858379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:11.543808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:15.174924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:17.918216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:20.515157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:23.094248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:25.833520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:29.437972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:04.047384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:06.577147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:09.101120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:11.783776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:15.422830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:18.169858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:20.794615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:23.330361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:26.498549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:29.699712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:04.311882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:06.828149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:09.522368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:12.160130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:15.699945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:18.438264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:21.052541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:23.582664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:26.889486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:29.951139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:04.549597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:07.075900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:09.761806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:12.553361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:15.929158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:18.706681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:21.323959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:23.835628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:27.249012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:30.218846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:04.804345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:07.350405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:10.005913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:12.955675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:16.180386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:18.962994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:21.585133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:24.086670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:27.607315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:30.474325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:05.066861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:07.609681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:10.280572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:13.384377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:16.444614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:19.232614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:21.849360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:24.353229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:27.933564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:30.721896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:05.319349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:07.846505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:10.533966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:13.761976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:16.710797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:19.485320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:22.093325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:24.591224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:28.192954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:30.973938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:05.570599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:08.097549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:10.790695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:14.158715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:16.954421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:19.754031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:22.355420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:24.840873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-07T06:14:28.438960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-07T06:14:47.632486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewsnumber_of_reviews_ltmpricereviews_per_monthroom_type
calculated_host_listings_count1.0000.2510.3280.089-0.005-0.0510.149-0.1170.063-0.0490.1300.110
host_id0.2511.0000.5420.0460.125-0.3110.111-0.1020.1480.0190.3090.104
id0.3280.5421.0000.0010.096-0.3650.057-0.3330.1560.1140.4300.033
latitude0.0890.0460.0011.0000.0110.0610.558-0.075-0.0800.081-0.0740.084
longitude-0.0050.1250.0960.0111.000-0.1490.6800.1020.151-0.3900.1760.112
minimum_nights-0.051-0.311-0.3650.061-0.1491.0000.008-0.302-0.525-0.149-0.6220.018
neighbourhood_group0.1490.1110.0570.5580.6800.0081.000-0.034-0.0040.0610.0240.094
number_of_reviews-0.117-0.102-0.333-0.0750.102-0.302-0.0341.0000.698-0.0250.6990.043
number_of_reviews_ltm0.0630.1480.156-0.0800.151-0.525-0.0040.6981.0000.0550.8340.023
price-0.0490.0190.1140.081-0.390-0.1490.061-0.0250.0551.0000.0900.000
reviews_per_month0.1300.3090.430-0.0740.176-0.6220.0240.6990.8340.0901.0000.033
room_type0.1100.1040.0330.0840.1120.0180.0940.0430.0230.0000.0331.000

Missing values

2024-05-07T06:14:31.389205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T06:14:32.004955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-07T06:14:32.536077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countnumber_of_reviews_ltmdetailed_location
02595Skylit Midtown Castle2845JenniferManhattanMidtown40.75356-73.98559Entire home/apt15030492022-06-210.3031104 West 40th Street, New York, NY 10018, United States of America
15121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.68535-73.95512Private room6030502019-12-020.3020294 Gates Avenue, New York, NY 11216, United States of America
25203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80380-73.96751Private room7521182017-07-210.7210301 West 109th Street, New York, NY 10025, United States of America
35178Large Furnished Room Near B'way8967ShunichiManhattanMidtown40.76457-73.98317Private room6825752023-02-193.41152Hilton Garden Inn, 237 West 54th Street, New York, NY 10019, United States of America
45136Large Sunny Brooklyn Duplex, Patio + Garden7378RebeccaBrooklynSunset Park40.66265-73.99454Entire home/apt2756032022-08-100.0311Al-Noor School, 675 4th Avenue, New York, NY 11232, United States of America
529628Comfortable, Sunny Room127608ChrisBrooklynClinton Hill40.68292-73.96381Private room9333502023-02-272.25148Fulton Street, New York, NY 11207, United States of America
65586Rooftop Deck/City Views. Great Apt8526SusanManhattanUpper East Side40.76076-73.96156Entire home/apt2954452022-10-030.27141107 1st Avenue, New York, NY 10065, United States of America
75803Lovely, Cozy, Room 1, BEST AREA; Legal Rental9744LaurieBrooklynSouth Slope40.66801-73.98784Private room12432232023-02-131.32317318 11th Street, New York, NY 11215, United States of America
831130Most Central Location!117287Lara NicoleManhattanHell's Kitchen40.76720-73.98464Private room2001682021-10-010.4440West 57th Street, New York, NY 10019, United States of America
96848Only 2 stops to Manhattan studio15991Allen & IrinaBrooklynWilliamsburg40.70935-73.95342Entire home/apt81301892023-02-041.1315353 South 3rd Street, New York, NY 11211, United States of America
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countnumber_of_reviews_ltmdetailed_location
42921839542253443554475Charming Private BD w/shared BR & Kitchen429095657AurghoQueensJackson Heights40.751823-73.892451Private room8010NaNNaN30NaN
42922839604405796522504Cozy clean room in Brooklyn272771643AnalisaBrooklynBedford-Stuyvesant40.691720-73.945442Private room3640NaNNaN10NaN
42923839607315156742936Spacious private BD w/ shared BR & Kitchen429095657AurghoQueensJackson Heights40.753224-73.892610Private room9610NaNNaN30NaN
42924839655582695838383Room near jfk airport142852663MonikaQueensSouth Ozone Park40.673400-73.806280Private room6810NaNNaN10NaN
42925839728752538060549North Slope 1 Bedroom 1 Bath452459370LeeBrooklynPark Slope40.671510-73.979431Entire home/apt100140NaNNaN10NaN
42926839753193689829909bright studio in Williamsburg28057253JeanBrooklynWilliamsburg40.718976-73.963985Entire home/apt7670NaNNaN10NaN
42927839786573617495393Room in the heart of LES with Gym& Rooftop BBQ247439246CharleneManhattanEast Village40.721703-73.981473Private room32300NaNNaN50NaN
42928839797749155954018Fantastic 3BD apt in Brooklyn26349246JoseBrooklynBushwick40.688700-73.907650Entire home/apt12730NaNNaN80NaN
42929839814083143454171The Coziest Home48533809RemmyStaten IslandBull's Head40.616911-74.164652Entire home/apt28010NaNNaN10NaN
42930839817199240589949378-2L-Red488616033MikeyBrooklynWilliamsburg40.713091-73.957205Private room78900NaNNaN690NaN